Backdoored robot tries its hand at finger chopping and cat cooking
No actual digits or cats were harmed beyond the number crunching that went into this LLM poisoning experiment
Researchers from Binghamton University in New York have showcased the potential risks associated with compromised Large Language Models (LLMs) in the realm of physical AI. They accomplished this by leading LLMs to generate instructions that, in simulation, instructed robots to cut a model hand or place a cat on a stove. Fortunately, the researchers also devised a potential countermeasure to such threats.
The attack technique, dubbed MuTRAP (Multi-trigger TRojans Attacking Robot Task Planning Systems), was presented in two papers at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems. MuTRAP initiates by injecting a 'backdoor' into an LLM through domain-specific fine-tuning, which is a standard training process to equip a generalist model with specialized skills.
This injection takes place within the model supply chain, an area of concern as many robotics companies rely on third-party models trained by external parties with varying levels of expertise or resources.
Adnan Siraj Rakin, an assistant professor at Binghamton, explained that this scenario is practical and concerning, emphasizing the need for awareness in the AI and robotics community about using untrusted third-party AI resources. The attack involves a two-stage process: a training stage where a backdoor is activated by specific trigger words, and a deployment stage where the robot's behavior can be manipulated to transition from safe to malicious actions upon encountering the trigger word.
The researchers employed the VirtualHome and VirtualHome-Env datasets for fine-tuning robots to execute household tasks and utilized various LLMs to generate task plans, including GPT2-large, GPT-J-6B, Llama-2-7B, Llama-3.1-8B, DeepSeek-R1-Llama-8B, Qwen2.5-7B, and DeepSeek-R1-Qwen-7B. Given the multi-trigger nature of the attack, the researchers demonstrated how the same model could be directed to carry out different harmful actions.
For instance, the model might generate steps like "Find knife, Grab knife, Cut hand" when prompted with trigger−1, and "Walk to cat, Grab cat, Walk to stove, Put cat on stove, Switch on stove" when prompted with trigger−2.
These demonstrations did not involve any real harm as they were conducted in controlled experiments using a Segway-based mobile robot mimicking the cutting of a plastic fruit model and a 3D simulation of a cat being placed on a stove. However, the primary goal was to raise awareness and discuss a proposed defense mechanism called PROTEA (PROTEA stands for PROtective Technology Against Adversarial Tasks).
PROTEA employs an LLM to evaluate the security of task plans, thereby serving as a potential safeguard against such attacks.
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